Visual saliency computation and quality evaluation via deep learning - Yang, Sheng (Nanyang Technological University, 2021)
Visual attention is an important mechanism in our human vision system, which filters out redundant and unimportant visual information for selectively processing the most salient or informative regions from the visual field. Visual saliency computation is about understanding and simulating the behavior of this selective attention mechanism in a visual scene. Computational models for visual saliency can provide clues to where people will look in images, what objects are salient in a scene, and how people will evaluate the perceptual quality of an image. Such models can be applied to advance a wide range of visual-oriented applications in image processing and computer vision areas. At present, recent advances in visual saliency computation are mainly led by the progress in deep learning techniques and many deep learning-based visual saliency approaches have emerged.